Continual Learning on Graphs: A Survey
It identifies a key limitation in a growing research area (continual graph learning) for machine learning practitioners, but is incremental as it synthesizes existing work rather than proposing new methods.
This survey paper addresses the gap in continual graph learning research, which has focused on mitigating catastrophic forgetting but ignored continuous performance improvement, by providing a comprehensive taxonomy and analysis of challenges and solutions.
Recently, continual graph learning has been increasingly adopted for diverse graph-structured data processing tasks in non-stationary environments. Despite its promising learning capability, current studies on continual graph learning mainly focus on mitigating the catastrophic forgetting problem while ignoring continuous performance improvement. To bridge this gap, this article aims to provide a comprehensive survey of recent efforts on continual graph learning. Specifically, we introduce a new taxonomy of continual graph learning from the perspective of overcoming catastrophic forgetting. Moreover, we systematically analyze the challenges of applying these continual graph learning methods in improving performance continuously and then discuss the possible solutions. Finally, we present open issues and future directions pertaining to the development of continual graph learning and discuss how they impact continuous performance improvement.